{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting nodes and edges with saliency maps in GAT\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/interpretability/gat-node-link-importance.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/interpretability/gat-node-link-importance.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo shows how to use integrated gradients in graph attention networks to obtain accurate importance estimations for both the nodes and edges. The notebook consists of three parts:\n",
    "\n",
    "setting up the node classification problem for Cora citation network\n",
    "training and evaluating a GAT model for node classification\n",
    "calculating node and edge importances for model's predictions of query (\"target\") nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import os\n",
    "import time\n",
    "import sys\n",
    "import stellargraph as sg\n",
    "from copy import deepcopy\n",
    "\n",
    "\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GAT, GraphAttention\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, models, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from tensorflow.keras import backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the CORA network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "G, subjects = dataset.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5429\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Features: float32 vector, length 1433\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5429]\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_subjects, test_subjects = model_selection.train_test_split(\n",
    "    subjects, train_size=140, test_size=None, stratify=subjects\n",
    ")\n",
    "val_subjects, test_subjects = model_selection.train_test_split(\n",
    "    test_subjects, train_size=500, test_size=None, stratify=test_subjects\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Theory': 18,\n",
       "         'Neural_Networks': 42,\n",
       "         'Genetic_Algorithms': 22,\n",
       "         'Case_Based': 16,\n",
       "         'Rule_Learning': 9,\n",
       "         'Probabilistic_Methods': 22,\n",
       "         'Reinforcement_Learning': 11})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = preprocessing.LabelBinarizer()\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_subjects)\n",
    "val_targets = target_encoding.transform(val_subjects)\n",
    "test_targets = target_encoding.transform(test_subjects)\n",
    "\n",
    "all_targets = target_encoding.transform(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GAT model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. Since GAT is a full-batch model, we use the `FullBatchNodeGenerator` class to feed node features and graph adjacency matrix to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = FullBatchNodeGenerator(G, method=\"gat\", sparse=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_subjects.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use two GAT layers with 8-dimensional hidden node features at each layer.\n",
    " * `attn_heads` is the number of attention heads in all but the last GAT layer in the model\n",
    " * `activations` is a list of activations applied to each layer's output\n",
    " * Arguments such as `bias`, `in_dropout`, `attn_dropout` are internal parameters of the model, execute `?GAT` for details. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To follow the GAT model architecture used for Cora dataset in the original paper [Graph Attention Networks. P. Veličković et al. ICLR 2018 https://arxiv.org/abs/1803.07294], let's build a 2-layer GAT model, with the second layer being the classifier that predicts paper subject: it thus should have the output size of `train_targets.shape[1]` (7 subjects) and a softmax activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gat = GAT(\n",
    "    layer_sizes=[8, train_targets.shape[1]],\n",
    "    attn_heads=8,\n",
    "    generator=generator,\n",
    "    bias=True,\n",
    "    in_dropout=0,\n",
    "    attn_dropout=0,\n",
    "    activations=[\"elu\", \"softmax\"],\n",
    "    normalize=None,\n",
    "    saliency_map_support=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expose the input and output tensors of the GAT model for node prediction, via GAT.in_out_tensors() method:\n",
    "x_inp, predictions = gat.in_out_tensors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `predictions` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=predictions)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    weighted_metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_subjects.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1/1 - 9s - loss: 1.9274 - acc: 0.1571 - val_loss: 1.7515 - val_acc: 0.3960\n",
      "Epoch 2/10\n",
      "1/1 - 2s - loss: 1.6477 - acc: 0.5357 - val_loss: 1.5972 - val_acc: 0.4440\n",
      "Epoch 3/10\n",
      "1/1 - 2s - loss: 1.4080 - acc: 0.6429 - val_loss: 1.4644 - val_acc: 0.5160\n",
      "Epoch 4/10\n",
      "1/1 - 2s - loss: 1.1955 - acc: 0.7500 - val_loss: 1.3436 - val_acc: 0.5660\n",
      "Epoch 5/10\n",
      "1/1 - 2s - loss: 1.0033 - acc: 0.8000 - val_loss: 1.2316 - val_acc: 0.6240\n",
      "Epoch 6/10\n",
      "1/1 - 2s - loss: 0.8298 - acc: 0.8643 - val_loss: 1.1291 - val_acc: 0.6620\n",
      "Epoch 7/10\n",
      "1/1 - 2s - loss: 0.6770 - acc: 0.9143 - val_loss: 1.0381 - val_acc: 0.7060\n",
      "Epoch 8/10\n",
      "1/1 - 2s - loss: 0.5456 - acc: 0.9500 - val_loss: 0.9591 - val_acc: 0.7480\n",
      "Epoch 9/10\n",
      "1/1 - 2s - loss: 0.4348 - acc: 0.9643 - val_loss: 0.8916 - val_acc: 0.7540\n",
      "Epoch 10/10\n",
      "1/1 - 2s - loss: 0.3428 - acc: 0.9714 - val_loss: 0.8356 - val_acc: 0.7700\n"
     ]
    }
   ],
   "source": [
    "N = G.number_of_nodes()\n",
    "history = model.fit(\n",
    "    train_gen, validation_data=val_gen, shuffle=False, epochs=10, verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.utils.plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the trained model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.7781\n",
      "\tacc: 0.7955\n"
     ]
    }
   ],
   "source": [
    "test_gen = generator.flow(test_subjects.index, test_targets)\n",
    "\n",
    "test_metrics = model.evaluate(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check serialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model\n",
    "model_json = model.to_json()\n",
    "model_weights = model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load model from json & set all weights\n",
    "model2 = models.model_from_json(model_json, custom_objects=sg.custom_keras_layers)\n",
    "model2.set_weights(model_weights)\n",
    "model2_weights = model2.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "pred2 = model2.predict(test_gen)\n",
    "pred1 = model.predict(test_gen)\n",
    "print(np.allclose(pred1, pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node and link importance via saliency maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define the importances of node features, nodes, and links in the target node's neighbourhood (ego-net), and evaluate them using our library.\n",
    "\n",
    "Node feature importance: given a target node $t$ and the model's prediction of $t$'s class, for each node $v$ in its ego-net, feature importance of feature $f$ for node $v$ is defined as the change in the target node's predicted score $s(c)$ for the winning class $c$ if feature $f$ of node $v$ is perturbed.\n",
    "\n",
    "The overall node importance for node $v$ is defined here as the sum of all feature importances for node $v$, i.e., it is the amount by which the target node's predicted score $s(c)$ would change if we set all features of node $v$ to zeros.\n",
    "\n",
    "Link importance for link $e=(u, v)$ is defined as the change in target node $t$'s predicted score $s(c)$ if the link $e$ is removed from the graph. Links with high importance (positive or negative) affect the target node prediction more than links with low importance.\n",
    "\n",
    "Node and link importances can be used to assess the role of neighbour nodes and links in model's predictions for the node(s) of interest (the target nodes). For datasets like CORA-ML, the features and edges are binary, vanilla gradients may not perform well so we use integrated gradients to compute them (https://arxiv.org/pdf/1703.01365.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stellargraph.interpretability.saliency_maps import IntegratedGradientsGAT\n",
    "from stellargraph.interpretability.saliency_maps import GradientSaliencyGAT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the target node whose prediction is to be interpreted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_nodes = list(G.nodes())\n",
    "all_gen = generator.flow(graph_nodes)\n",
    "target_nid = 1109199\n",
    "target_idx = graph_nodes.index(target_nid)\n",
    "target_gen = generator.flow([target_nid])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Node id of the target node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = all_targets[target_idx]  # true class of the target node"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract adjacency matrix and feature matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target node id: 1109199, \n",
      "true label: [0 1 0 0 0 0 0], \n",
      "predicted label: [0.05 0.75 0.06 0.02 0.06 0.02 0.03]\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(target_gen).squeeze()\n",
    "class_of_interest = np.argmax(y_pred)\n",
    "print(\n",
    "    \"target node id: {}, \\ntrue label: {}, \\npredicted label: {}\".format(\n",
    "        target_nid, y_true, y_pred.round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node feature importance by using integrated gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_grad_saliency = IntegratedGradientsGAT(model, train_gen, generator.node_list)\n",
    "saliency = GradientSaliencyGAT(model, train_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the ego network of the target node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ego = nx.ego_graph(G.to_networkx(), target_nid, radius=len(gat.activations))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the link importance by integrated gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "integrated_link_mask.shape = (2708, 2708)\n"
     ]
    }
   ],
   "source": [
    "integrate_link_importance = int_grad_saliency.get_link_importance(\n",
    "    target_nid, class_of_interest, steps=25\n",
    ")\n",
    "print(\"integrated_link_mask.shape = {}\".format(integrate_link_importance.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "\n",
      "integrated_node_importance [0. 0. 0. ... 0. 0. 0.]\n",
      "integrated self-importance of target node 1109199: 0.47\n",
      "\n",
      "Ego net of target node 1109199 has 202 nodes\n",
      "Number of non-zero elements in integrated_node_importance: 212\n"
     ]
    }
   ],
   "source": [
    "integrated_node_importance = int_grad_saliency.get_node_importance(\n",
    "    target_nid, class_of_interest, steps=25\n",
    ")\n",
    "print(\"\\nintegrated_node_importance\", integrated_node_importance.round(2))\n",
    "print(\n",
    "    \"integrated self-importance of target node {}: {}\".format(\n",
    "        target_nid, integrated_node_importance[target_idx].round(2)\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    \"\\nEgo net of target node {} has {} nodes\".format(target_nid, G_ego.number_of_nodes())\n",
    ")\n",
    "print(\n",
    "    \"Number of non-zero elements in integrated_node_importance: {}\".format(\n",
    "        np.count_nonzero(integrated_node_importance)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the ranks of the edge importance values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_indices = np.argsort(integrate_link_importance.flatten().reshape(-1))\n",
    "sorted_indices = np.array(sorted_indices)\n",
    "integrated_link_importance_rank = [(int(k / N), k % N) for k in sorted_indices[::-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 most important links by integrated gradients are [(1544, 163), (1206, 163), (1544, 1206), (1206, 789), (163, 219), (163, 163), (566, 294), (566, 733), (163, 1136), (163, 1098)]\n"
     ]
    }
   ],
   "source": [
    "topk = 10\n",
    "print(\n",
    "    \"Top {} most important links by integrated gradients are {}\".format(\n",
    "        topk, integrated_link_importance_rank[:topk]\n",
    "    )\n",
    ")\n",
    "# print('Top {} most important links by integrated gradients (for potential edges) are {}'.format(topk, integrated_link_importance_rank_add[-topk:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we plot the link and node importance (computed by integrated gradients) of the nodes within the ego graph of the target node.\n",
    "\n",
    "For nodes, the shape of the node indicates the positive/negative importance the node has. 'round' nodes have positive importance while 'diamond' nodes have negative importance. The size of the node indicates the value of the importance, e.g., a large diamond node has higher negative importance.\n",
    "\n",
    "For links, the color of the link indicates the positive/negative importance the link has. 'red' links have positive importance while 'blue' links have negative importance. The width of the link indicates the value of the importance, e.g., a thicker blue link has higher negative importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(G_ego, values={x[0]: {\"subject\": x[1]} for x in subjects.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_size_factor = 1e2\n",
    "link_width_factor = 4\n",
    "\n",
    "nodes = list(G_ego.nodes())\n",
    "colors = pd.DataFrame(\n",
    "    [v[1][\"subject\"] for v in G_ego.nodes(data=True)], index=nodes, columns=[\"subject\"]\n",
    ")\n",
    "colors = np.argmax(target_encoding.transform(colors), axis=1) + 1\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "pos = nx.spring_layout(G_ego)\n",
    "# Draw ego as large and red\n",
    "node_sizes = [integrated_node_importance[graph_nodes.index(k)] for k in nodes]\n",
    "node_shapes = [\n",
    "    \"o\" if integrated_node_importance[graph_nodes.index(k)] > 0 else \"d\" for k in nodes\n",
    "]\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "# node_size_sclae is used for better visualization of nodes\n",
    "node_size_scale = node_size_factor / np.max(node_sizes)\n",
    "for k in range(len(node_shapes)):\n",
    "    if list(nodes)[k] == target_nid:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(list(nodes)[k])\n",
    "        positive_node_sizes.append(node_size_scale * node_sizes[k])\n",
    "\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(list(nodes)[k])\n",
    "        negative_node_sizes.append(node_size_scale * abs(node_sizes[k]))\n",
    "\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_nid)]),\n",
    "    node_shape=\"*\",\n",
    "    node_color=[colors[nodes.index(target_nid)]],\n",
    "    cmap=cmap,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "\n",
    "edges = G_ego.edges()\n",
    "# link_width_scale is used for better visualization of links\n",
    "weights = [\n",
    "    integrate_link_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "link_width_scale = link_width_factor / np.max(weights)\n",
    "edge_colors = [\n",
    "    \"red\"\n",
    "    if integrate_link_importance[graph_nodes.index(u), graph_nodes.index(v)] > 0\n",
    "    else \"blue\"\n",
    "    for u, v in edges\n",
    "]\n",
    "\n",
    "ec = nx.draw_networkx_edges(\n",
    "    G_ego, pos, edge_color=edge_colors, width=[link_width_scale * w for w in weights]\n",
    ")\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then remove the node or edge in the ego graph one by one and check how the prediction changes. By doing so, we can obtain the ground truth importance of the nodes and edges. Comparing the following figure and the above one can show the effectiveness of integrated gradients as the importance approximations are relatively consistent with the ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "[X, _, A], y_true_all = all_gen[0]\n",
    "N = A.shape[-1]\n",
    "X_bk = deepcopy(X)\n",
    "edges = [(graph_nodes.index(u), graph_nodes.index(v)) for u, v in G_ego.edges()]\n",
    "nodes_idx = [graph_nodes.index(v) for v in nodes]\n",
    "selected_nodes = np.array([[target_idx]], dtype=\"int32\")\n",
    "clean_prediction = model.predict([X, selected_nodes, A]).squeeze()\n",
    "predict_label = np.argmax(clean_prediction)\n",
    "groud_truth_edge_importance = np.zeros((N, N), dtype=\"float\")\n",
    "groud_truth_node_importance = []\n",
    "\n",
    "for node in nodes_idx:\n",
    "    if node == target_idx:\n",
    "        groud_truth_node_importance.append(0)\n",
    "        continue\n",
    "    X = deepcopy(X_bk)\n",
    "    # we set all the features of the node to zero to check the ground truth node importance.\n",
    "    X[0, node, :] = 0\n",
    "    predict_after_perturb = model.predict([X, selected_nodes, A]).squeeze()\n",
    "    prediction_change = (\n",
    "        clean_prediction[predict_label] - predict_after_perturb[predict_label]\n",
    "    )\n",
    "    groud_truth_node_importance.append(prediction_change)\n",
    "\n",
    "node_shapes = [\n",
    "    \"o\" if groud_truth_node_importance[k] > 0 else \"d\" for k in range(len(nodes))\n",
    "]\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "# node_size_scale is used for better visulization of nodes\n",
    "node_size_scale = node_size_factor / max(groud_truth_node_importance)\n",
    "\n",
    "for k in range(len(node_shapes)):\n",
    "    if nodes_idx[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(graph_nodes[nodes_idx[k]])\n",
    "        positive_node_sizes.append(node_size_scale * groud_truth_node_importance[k])\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(graph_nodes[nodes_idx[k]])\n",
    "        negative_node_sizes.append(node_size_scale * abs(groud_truth_node_importance[k]))\n",
    "\n",
    "X = deepcopy(X_bk)\n",
    "for edge in edges:\n",
    "    original_val = A[0, edge[0], edge[1]]\n",
    "    if original_val == 0:\n",
    "        continue\n",
    "    # we set the weight of a given edge to zero to check the ground truth link importance\n",
    "    A[0, edge[0], edge[1]] = 0\n",
    "    predict_after_perturb = model.predict([X, selected_nodes, A]).squeeze()\n",
    "    groud_truth_edge_importance[edge[0], edge[1]] = (\n",
    "        predict_after_perturb[predict_label] - clean_prediction[predict_label]\n",
    "    ) / (0 - 1)\n",
    "    A[0, edge[0], edge[1]] = original_val\n",
    "#     print(groud_truth_edge_importance[edge[0], edge[1]])\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes_idx.index(target_idx)]),\n",
    "    node_color=[colors[nodes_idx.index(target_idx)]],\n",
    "    cmap=cmap,\n",
    "    node_shape=\"*\",\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "# Draw the ego net\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "edges = G_ego.edges()\n",
    "# link_width_scale is used for better visulization of links\n",
    "link_width_scale = link_width_factor / np.max(groud_truth_edge_importance)\n",
    "weights = [\n",
    "    link_width_scale\n",
    "    * groud_truth_edge_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "\n",
    "edge_colors = [\n",
    "    \"red\"\n",
    "    if groud_truth_edge_importance[graph_nodes.index(u), graph_nodes.index(v)] > 0\n",
    "    else \"blue\"\n",
    "    for u, v in edges\n",
    "]\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
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    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/interpretability/gat-node-link-importance.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/interpretability/gat-node-link-importance.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
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